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A graphical diagnostic for heavy tailed data
Author(s) -
Nolan John P.
Publication year - 2020
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2590
Subject(s) - univariate , multivariate statistics , computer science , graphical model , statistical graphics , data mining , statistics , artificial intelligence , mathematics , machine learning , graphics , computer graphics (images)
Graphical diagnostics are described for general heavy tailed data. This tool allows for a model free assessment of the tails of a univariate dataset a transform on the tails of the data. In addition, one can add to the basic plots comparisons of a dataset to multiple models. Multivariate extensions are described using an ordering based on distance from a center.
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